Are You A Victim of Data Quality Procrastination?

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Data Quality Procrastination, or DQP, affects 63% of organizations,
who say that they lack a “coherent, centralized approach” to their data
quality strategy. This is a shocking statistic seeing as 99% of organizations believe that data is essential for marketing success, and that 83%
of commercial companies believe their revenue is affected by inaccurate
and incomplete customer or prospect data. Not to mention that the
implementation of a data quality initiative can lead to 15-20% increased
revenue, 20-40% increased sales, and a 40% decrease in operating costs (source).

How do you know if you’re suffering from Data Quality Procrastination?

Symptoms of DQP

Unhappy Customers

Data Quality Procrastination is an exceptionally harmful condition,
because it affects more than just your revenue. Customers are often the
unsuspecting victims of DQP, and their disappointment in mis-targeted or
inaccurate marketing is detrimental to their satisfaction rate. A 2014
study found that 94% of respondents
reported taking at least one of the below actions in response to a
company that consistently mis-targeted them in their email marketing
efforts as a result of inaccurate data.

Consumers want to be marketed to with personalized, relevant content. Seventy-three percent of consumers prefer brands that use their personal information to make their shopping experiences more relevant, and 86% of consumers
say personalization plays a role in their purchasing decisions. But,
unfortunately, consumers of companies with Data Quality Procrastination
often go without the personalized service they long for, as companies
without a solid data quality management plan are unable to keep up with
the constant evolution of consumer information. The longer Data Quality
Procrastination goes untreated the more your database will decay,
generally at a rate of 25% for B2C and 70% for B2B per year, which
continues to erode at consumer opinion of your brand.

Wasted Time and Money

The average company loses 12% of revenue due to bad data, and as much
as 50% of a typical IT budget can be spent on “information scrap and
rework” (source).
The four basic characteristics that negatively impact data quality are
inaccuracies, duplications, gaps, and outdated information. If a record
has any of these characteristics it decreases the quality of your
database as a whole, which can mean ineffective outreach and missed
opportunities.

There are many studies that attempt to quantify exactly how much 1 bad record will cost; some say it’s ten times more expensive to complete a unit of simple work with bad data, and others say it costs about $1 to verify a record as it is entered,
about $10 dollars to fix it later, and $100 if nothing is done, as the
ramifications of the mistakes are felt over and over again. While it’s
difficult to exactly pin down a dollar amount, there are ramifications
that go even beyond financial revenue.

Employee morale is a lesser known victim of low data quality as
frustrations with wrong or missing information arise or workloads are
increased because of inadequate databases. Often also, internal trust
between departments can be damaged in situations where one department
requests data from another and the delivered information is found to be
incorrect, even if the delivering department had nothing to do with the
data input or management.

The average company wastes $180,000 per year simply on direct mail that is returned because of inaccurate data. Considering the fact that about 36 million
people move each year, that cost most likely doesn’t even begin to
cover the amount of direct mail that is received by an incorrect
recipient and thrown away. The Econsultancy/Adestra Email Marketing Census found that 67% of surveyed businesses reported problems delivering email, and more than half
of respondents blamed overall problems with their email campaigns on
low quality data. It’s safe to say that the 50% who said this are also
suffering from Data Quality Procrastination. Lastly, if you’re of the 22% who feel their contact data is inaccurate and your phone calls often hit a disconnected line, you may be suffering from Data Quality Procrastination.

Other telltale signs of Data Quality Procrastination:

Keeping data in multiple, disconnected silos

Conducting meetings that start with the question “How did we not know that?”

Reading articles on data quality, but never advancing to the next step

Ever getting these responses when contacting a prospect or customer:

Don’t be a statistic. If you or a loved one has been diagnosed with Data Quality Procrastination, please don’t hesitate to get your data’s condition under control.